Diligent upfront planning holds the key to success, says Daniel Hewson, data capability manager for Elecosoft
You’re the CEO of a construction company that has decided to embrace the power of artificial intelligence (AI). You’ve learned from an almost unlimited number of sources, including media outlets, that AI can play an integral role in improving productivity, fostering innovation and leading to a stronger bottom line.
The time has come to act. You’re ready to go.
Not so fast.
It’s true that the construction industry has begun to realise the transformative potential of AI. The market forecasts bear this out: according to Adroit Market Research, the global market for AI in construction was estimated in 2022 to be worth $1.3bn, and by 2030, it is anticipated to grow to $13.5bn, with an eye-popping compound annual growth rate (CAGR) of 36.03%.
Clearly, the construction industry is bullish on the idea that AI will assume a prominent spot in most companies’ arsenals. However, integrating AI is not as simple as plugging in a new software system. It requires careful preparation, robust infrastructure and, most critically, clean data.
Companies eager to jump into the AI space must understand that AI is not a magic solution but a tool that needs a solid foundation to function effectively.
Many companies express interest in adopting AI, expecting immediate benefits. However, successful AI integration requires a proactive approach.
Before diving into AI, organisations must establish clear objectives. What problems do they want AI to solve? What kind of useful information do they hope to extract?
These questions are crucial as they undertake the preparation phase – doing all of the things to get their data house in order before any kind of AI initiative can be implemented.
In other words, identify what they want to do or accomplish with AI. Then work backwards to lay a technological foundation that will accept the AI tools that can meet your objectives.
Establishing objectives and assessing data
The first step is to determine the objectives for AI implementation. Companies need to review their current data and processes critically. Are they capturing the necessary information? Often, they will find that while they have substantial data, it might not be adequate or appropriately organised for coordination with AI tools. This is a common scenario where data quality and completeness become significant hurdles.
There’s a saying in the world of artificial intelligence: “AI is the only place where B comes before A.” What it means is that before you can begin integrating AI, companies need to focus on BI, or business intelligence.
This involves getting an analyst to scrutinise existing data to solve your target problems manually. If a human analyst can derive valuable insights from the data, it indicates that the data is robust enough for AI. This step serves as a proof of concept, demonstrating that the data can indeed solve the intended problems.
Conversely, if a business analyst can’t actually look at your information, build some sort of mental model and extract answers to any pertinent questions from that data then there’s little chance that AI, despite all the hype, will be able to produce any meaningful results.
It is critical to understand that AI solutions scale well in terms of cost but are almost never as accurate as a detailed human analyst.
Another thing to keep in mind is that a great deal of the data and information that companies have accrued might have been collected for a specific use case that doesn’t lend itself to the use of AI.
It might have even been stored with no real purpose in mind – it’s not uncommon to hold on to data that never sees the light of day.
Data cleaning and process improvement
A critical aspect of preparing for AI is data cleaning. Industry figures suggest that a whopping 80% of accumulated data is “dirty data,” unclean and, consequently, unusable.
This necessitates a thorough review and cleaning process, ensuring that only high-quality data is fed into AI systems.
AI models suffer from GIGO, or Garbage In, Garbage Out, hence the criticality of the data cleaning process. Additionally, companies need to put significant effort into refining their data collection and storage processes to maintain high data quality.
For companies dealing with planning data, having a central repository is vital. This eliminates the common issue of multiple, fragmented versions of data files, ensuring that the most accurate and final versions are the ones used for analysis.
Once the data is clean, the next step is to enrich it with additional layers of information. For example, in construction planning, adding metadata can help provide deeper insights. This iterative process involves regular reviews and enhancements, gradually building a robust dataset that AI can leverage effectively.
The importance of business intelligence and BIM
Companies that have already invested in business intelligence and BIM (Building Information Modelling) will enjoy a marked advantage.
These tools provide a solid foundation for AI by ensuring data is significantly richer and considerably easier to make machine-readable. In addition to BIM, a thoughtful approach to BI means that metrics are not just collected for the sake of it but are used to derive actionable insights.
These tools will also begin to form the foundation of an effective AI roadmap, which requires collaboration between software providers and clients.
In industries like construction, where projects vary widely in scope and regulatory requirements, AI solutions need to be adaptable. This adaptability is achieved through enriched data and continuous feedback from real-world applications.
One of the primary applications of AI in construction is risk assessment and project planning. AI can use historical data to quantify risks, such as underestimating activity durations or identifying potential delays. This proactive risk management enables companies to address issues early, improving project outcomes and client communication.
The future of AI in construction
In the highly competitive construction industry, AI can be a game-changer. It reduces the risk of errors, enhances project accuracy and improves bidding processes.
Companies that adopt AI tools and methodologies will have a major advantage over those that do not, as AI-driven efficiencies will lead to better margins and more successful project completions – more projects delivered on time and within budget.
The integration of AI in construction is akin to the evolution that took place from performing manual computations to the use of calculators. It’s a subjective perspective, but it is no exaggeration to say that the leap from traditional construction planning methods to AI-driven processes is even more dramatic and transformative.
Companies that fail to adopt AI will find it increasingly difficult to compete, much like a logistics company refusing to use modern navigation tools. Certainly, trucking companies can use old-fashioned roadmaps to help them reach their destinations. But with the emergence of programs like Google Maps and Waze, why would anyone even consider it?
The timeframe
For construction companies, AI represents a significant opportunity to enhance efficiency and accuracy. However, the path to successful AI integration requires careful planning, robust data infrastructure and continuous process improvement.
By taking a proactive approach and leveraging existing BI and BIM tools – not to mention hiring analysts to put your data through rigorous testing to make sure it is clean, accurate and capable of punching out useful insights – companies can set the stage for AI to deliver its full potential.
But none of this is going to be quick. The timeline for AI integration varies based on the organisation’s size and readiness.
For larger organisations, the process can take up to a year or more, whereas smaller companies might complete it within a quarter. This timeline includes setting up the necessary infrastructure, cleaning data and iterating processes to ensure everything is in place for AI deployment.
But here’s the good news: at the end of this extensive process of defining your goals, hiring analysts to run proof-of-concept tests, cleaning your data and performing the myriad other tasks, you’re going to have a robust data set that is going to build up over time that you already know you can use.
And as you continue to collect data, you’ll already be in the habit of cleaning it properly to make it usable for your AI tools. And that’s when you’ll realise it was worth it.
